A Comprehensive Survey on Vector Database: Storage and Retrieval Technique, Challenge

A vector database is used to store high-dimensional data that cannot be characterized by traditional DBMS. Although there are not many articles describing existing or introducing new vector database architectures, the approximate nearest neighbor search problem behind vector databases has been studied for a long time, and considerable related algorithmic articles can be found in the literature. This article attempts to comprehensively review relevant algorithms to provide a general understanding of this booming research area. The basis of our framework categorises these studies by the approach of solving ANNS problem, respectively hash-based, tree-based, graph-based and quantization-based approaches. Then we present an overview of existing challenges for vector databases. Lastly, we sketch how vector databases can be combined with large language models and provide new possibilities

Beyond Title VII: Rethinking Race, Ex-Offender Status, and Employment Discrimination in the Information Age

More than sixty-five million people in the United States—more than one in four adults—have had some involvement with the criminal justice system that will appear on a criminal history report. A rapidly expanding, for-profit industry has developed to collect these records and compile them into electronic databases, offering employers an inexpensive and readily accessible means of screening prospective employees. Nine out of ten employers now inquire into the criminal history of job candidates, systematically denying individuals with a criminal record any opportunity to gain work experience or build their job qualifications. This is so despite the fact that many individuals with criminal records have never been convicted of a crime, as one-third of felony arrests never result in conviction. And criminal records databases routinely contain significant errors, including false positive identifications and sealed or expunged information. The negative impact of employers’ reliance on criminal records databases falls most heavily on Black and Latino populations, as studies show that the stigma of having a criminal record is significantly more damaging for racial minorities than for Whites. This criminal record “penalty” limits profoundly the chance of achieving gainful employment, creating new and vexing problems for regulators, employers, and minorities with criminal records. Our existing regulatory apparatus, which is grounded in Title VII of the Civil Rights Act of 1964 and the Fair Credit Reporting Act, is ill-equipped to resolve this emerging dilemma because it fails to address systematic information failures and the problem of stigma. This Article, therefore, proposes a new framework drawn from core aspects of anti-discrimination laws that govern health law, notably the Americans with Disabilities Act, and the Genetic Information Nondiscrimination Act. These laws were designed to regulate the flow of information that may form the basis of an adverse employment decision, seeking to prevent discrimination preemptively. More fundamentally, they conceptualize discrimination through the lens of social stigma, which is critical to understanding and prophylactically curbing the particular discrimination that results from dual criminal record and minority status. This health law framework attends to the interests of minorities with criminal records, allows for more robust enforcement of existing laws, and enables employers to make appropriate and equitable hiring decisions, without engaging in invidious discrimination or contributing to the establishment of a new, and potentially enduring, underclass

Tracking body and hands for gesture recognition: NATOPS aircraft handling signals database

We present a unified framework for body and hand tracking, the output of which can be used for understanding simultaneously performed body-and-hand gestures. The framework uses a stereo camera to collect 3D images, and tracks body and hand together, combining various existing techniques to make tracking tasks efficient. In addition, we introduce a multi-signal gesture database: the NATOPS aircraft handling signals. Unlike previous gesture databases, this data requires knowledge about both body and hand in order to distinguish gestures. It is also focused on a clearly defined gesture vocabulary from a real-world scenario that has been refined over many years. The database includes 24 body-and-hand gestures, and provides both gesture video clips and the body and hand features we extracted

An integrated approach to deliver OLAP for multidimensional Semantic Web Databases

Semantic Webs (SW) and web data have become increasingly important sources to support Business Intelligence (BI), but they are difficult to manage due to the exponential increase in their volumes, inconsistency in semantics and complexity in representations. On-Line Analytical Processing (OLAP) is an important tool in analysing large and complex BI data, but it lacks the capability of processing disperse SW data due to the nature of its design. A new concept with a richer vocabulary than the existing ones for OLAP is needed to model distributed multidimensional semantic web databases. A new OLAP framework is developed, with multiple layers including additional vocabulary, extended OLAP operators, and usage of SPARQL to model heterogeneous semantic web data, unify multidimensional structures, and provide new enabling functions for interoperability. The framework is presented with examples to demonstrate its capability to unify existing vocabularies with additional vocabulary elements to handle both informational and topological data in Graph OLAP. The vocabularies used in this work are: the RDF Cube Vocabulary (QB) – proposed by the W3C to allow multi-dimensional, mostly statistical, data to be published in RDF; and the QB4OLAP – a QB extension introducing standard OLAP operators. The framework enables the composition of multiple databases (e.g. energy consumptions and property market values etc.) to generate observations through semantic pipe-like operators. This approach is demonstrated through Use Cases containing highly valuable data collected from a real-life environment. Its usability is proved through the development and usage of semantic pipe-like operators able to deliver OLAP specific functionalities. To the best of my knowledge there is no available data modelling approach handling both informational and topological Semantic Web data, which is designed either to provide OLAP capabilities over Semantic Web databases or to provide a means to connect such databases for further OLAP analysis. The thesis proposes that the presented work provides a wider understanding of: ways to access Semantic Web data; ways to build specialised Semantic Web databases, and, how to enrich them with powerful capabilities for further Business Intelligence

Knowledge-preserving Certain Answers for SQL-like Queries

International audienceAnswering queries over incomplete data is based on finding answers that are certainly true, independently of how missing values are interpreted. This informal description has given rise to several different mathematical definitions of certainty. To unify them, a framework based on "explanations", or extra information about incomplete data, was recently proposed. It partly succeeded in justifying query answering methods for relational databases under set semantics, but had two major limitations. First, it was firmly tied to the set data model, and a fixed way of comparing incomplete databases with respect to their information content. These assumptions fail for reallife database queries in languages such as SQL that use bag semantics instead. Second, it was restricted to queries that only manipulate data, while in practice most analytical SQL queries invent new values, typically via arithmetic operations and aggregation. To leverage our understanding of the notion of certainty for queries in SQL-like languages, we consider incomplete databases whose information content may be enriched by additional knowledge. The knowledge order among them is derived from their semantics, rather than being fixed a priori. The resulting framework allows us to capture and justify existing notions of certainty, and extend these concepts to other data models and query languages. As natural applications, we provide for the first time a well-founded definition of certain answers for the relational bag data model and for valueinventing queries on incomplete databases, addressing the key shortcomings of previous approaches

Medicines management issues in dementia and coping strategies used by people living with dementia and family carers: a systematic review

Objectives: Medicines play a key role in the lives of people with dementia, primarily to manage symptoms. Managing medicines is complex for people with dementia and their family carers and can result in multiple problems leading to harm. We conducted a systematic review to identify and model medication issues experienced and coping strategies used by people with dementia and/or family carers. Methods: Eleven general databases and four systematic review databases were searched. Studies were quality assessed using an established framework and thematically analysed. Results: 21 articles were included in this study and four domains affecting medication use were identified: cognitive, medication, social and cultural and, knowledge/educational and communication. People with dementia reported medication issues in all four domains but few coping strategies were developed. Family carers reported issues and coping strategies related to the medication and knowledge/educational and communication domains. Common issues with regards to knowledge and communication about medicines remain unresolved. The ‘voices’ of people with dementia appeared largely missing from the literature so were in-depth understanding of how, whether and in which circumstances coping strategies work in managing medicines. Conclusions: Medicines management is a complex set of activities and although current coping strategies exists, these were primarily used by family carers or the person with dementia-carer dyad. Health and social care practitioners and researchers should seek to understand in-depth, the ‘mechanisms of action’ of existing coping strategies and actively involve people with dementia as co-producers of knowledge to underpin any further work on medicines management

Initial experiences in developing e-health solutions across Scotland

The MRC funded Virtual Organisations for Trials and Epidemiological Studies (VOTES) project is a collaborative effort between e-Science, clinical and ethical research centres across the UK including the universities of Oxford, Glasgow, Imperial, Nottingham and Leicester. The project started in September 2005 and is due to run for 3 years. The primary goal of VOTES is to develop a reusable Grid framework through which a multitude of clinical trials and epidemiological studies can be supported. The National e-Science Centre (NeSC) at the University of Glasgow are looking at developing the Scottish components of this framework. This paper presents the initial experiences in developing this framework and in accessing and using existing data sets, services and software across the NHS in Scotland

Image mining: trends and developments

[Abstract]: Advances in image acquisition and storage technology have led to tremendous growth in very large and detailed image databases. These images, if analyzed, can reveal useful information to the human users. Image mining deals with the extraction of implicit knowledge, image data relationship, or other patterns not explicitly stored in the images. Image mining is more than just an extension of data mining to image domain. It is an interdisciplinary endeavor that draws upon expertise in computer vision, image processing, image retrieval, data mining, machine learning, database, and artificial intelligence. In this paper, we will examine the research issues in image mining, current developments in image mining, particularly, image mining frameworks, state-of-the-art techniques and systems. We will also identify some future research directions for image mining